Method and device for processing radar signals

ABSTRACT

A method for processing radar signals, wherein said radar signals comprise digitized data received by at least one radar antenna, the method comprising (i) determining FFT results based on the digitized data received; and (ii) storing a first group of the FFT results, wherein the first group of FFT results comprises at least two portions, wherein a first portion of FFT results is stored with a first accuracy and a second portion of FFT results is stored with a second accuracy.

BACKGROUND OF THE INVENTION

Embodiments of the present invention relate to radar applications, inparticular an efficient way to process radar signals obtained by atleast one radar sensor, e.g., via at least one antenna. Processing radarsignals in this regard in particular refers to radar signals received bya sensor or an antenna.

Several radar variants are used in cars for various applications. Forexample, radar can be used for blind spot detection (parking assistant,pedestrian protection, cross traffic), collision mitigation, lane changeassist and adaptive cruise control. Numerous use case scenarios forradar appliances may be directed to different directions (e.g., back,side, front), varying angles (e.g., azimuth direction angle) and/ordifferent distances (short, medium or long range). For example, anadaptive cruise control may utilize an azimuth direction angle amountingto ±18 degrees, the radar signal is emitted from the front of the car,which allows a detection range up to several hundred meters.

A radar source emits a signal and a sensor detects a returned signal. Afrequency shift between the emitted signal and the detected signal(based on, e.g., a moving car emitting the radar signal) can be used toobtain information based on the reflection of the emitted signal.Front-end processing of the signal obtained by the sensor may comprise aFast Fourier Transform (FFT), which may result in a signal spectrum,i.e. a signal distributed across the frequency. The amplitude of thesignal may indicate an amount of echo, wherein a peak may represent atarget that needs to be detected and used for further processing, e.g.,adjust the speed of the car based on another car travelling in front.

Constant false alarm rejection (CFAR), also referred to as constantfalse alarm rate, is in particular known as a threshold method for FFTresult analysis which may be based on a signal power. CFAR allowsadapting a threshold to decide whether the FFT signal indicates apotential target. CFAR in particular considers background noise, clutterand interference. Several CFAR algorithms are known. For details,reference is made tohttp://en.wikipedia.org/wiki/Constant_false_alarm_rate.

CFAR algorithms are often complex and require a significant amount oftime and/or resources, e.g., costly computation power. In case they needseveral clock cycles to provide a result, post-processing becomesdelayed which results in a limited real-time (or nearly real-time)capability of the whole system.

SUMMARY

A first embodiment relates to a method for processing radar signals,wherein said radar signals comprise digitized data received by at leastone radar antenna, the method comprising:

-   -   determining FFT results based on the digitized data received;    -   storing a first group of the FFT results, wherein the first        group of FFT results comprises at least two portions, wherein a        first portion of FFT results is stored with a first accuracy and        a second portion of FFT results is stored with a second        accuracy.

A second embodiment relates to a device for processing radar signals

-   -   comprising a FFT engine for determining FFT results based on        digitized data received from at least one antenna;    -   comprising a compression engine for storing a first group of the        FFT results, wherein the first group of FFT results comprises at        least two portions, wherein a first portion of FFT results is        stored with a first accuracy and a second portion of FFT results        is stored with a second accuracy.

A third embodiment relates to a device for processing radar signals,wherein said radar signals comprise digitized data received by at leastone radar antenna, comprising:

-   -   means for determining FFT results based on the digitized data        received;    -   means for storing a first group of the FFT results, wherein the        first group of FFT results comprises at least two portions,        wherein a first portion of FFT results is stored with a first        accuracy and a second portion of FFT results is stored with a        second accuracy.

A fourth embodiment is directed to a computer program product directlyloadable into a memory of a digital processing device, comprisingsoftware code portions for performing the steps of the method describedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are shown and illustrated with reference to the drawings.The drawings serve to illustrate the basic principle, so that onlyaspects necessary for understanding the basic principle are illustrated.The drawings are not to scale. In the drawings the same referencecharacters denote like features.

FIG. 1 shows a schematic diagram comprising an exemplary radar systememitting radar signals and receiving returned radar signals;

FIG. 2 shows an exemplary flow diagram comprising steps of how data canbe processed in a radar application;

FIG. 3 shows an exemplary schematic architecture utilizing a combinationof an FFT engine and a Bin rejection engine;

FIG. 4 shows a diagram depicting a first example of how FFT bins can becompressed;

FIG. 5 shows another example of a progressive compression scheme for FFTbins;

FIG. 6 shows yet one more example of a progressive compression schemefor FFT bins.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In a radar processing environment, a radar source emits a signal and asensor detects a returned signal. The returned signal may be acquired ina time domain by at least one antenna, in particular by severalantennas. The returned signal may then be converted into the frequencydomain by conducting a Fast Fourier Transform (FFT), which may result ina signal spectrum, i.e. a signal distributed across the frequency.Frequency peaks may be used to determine potential targets, e.g., alonga moving direction of a vehicle.

A Discrete Fourier Transform (DFT) may be implemented in computers bynumerical algorithms or dedicated hardware. Such implementation mayemploy FFT algorithms. Hence, the terms “FFT” and “DFT” may be usedinterchangeably.

FIG. 1 shows a schematic diagram comprising an exemplary radar system101 emitting radar signals 102 and receiving returned radar signals 103.The radar system 101 determines a range 104, a velocity 105 and anazimuth angle 106 based on the returned radar signals 103.

By using several receiving antennas, a phase difference of the receivedreturned radar signals 103 may be used to determine the azimuth angle106 via a third stage FFT. A first stage FFT based on the receivedreturned (e.g., reflected emitted) radar signals 103 is used todetermine the range 104, a second stage FFT based on the range 104 isused to determine the velocity 105 and the third stage FFT based on thevelocity 105 is used to determine said azimuth angle 106.

In an exemplary scenario, the emitted radar signals 102 may beoriginated by two transmitter antennas towards an exemplary object. Thesignals 102 are reflected at the object and arrive at several (e.g.,four) receiving antennas dependent on the azimuth angle with differentphase position. Hence, the distances between the single object, thetransmitter antennas and the receiver antennas may be deemed different.

In case the single object is considerably far away from the antennas,the beam paths may be treated as being parallel to each other.

FIG. 2 shows an exemplary flow diagram comprising steps of how data canbe processed in a radar application. In a step 201, samples received bya sensor are stored. In a step 202, a first-stage FFT is conducted andin a step 203 the results are stored. In a step 204 an optional CFARalgorithm may be run on the data stored in the step 203. In a subsequentstep 205, a second stage FFT may be conducted on selected bins and in astep 206 a third stage FFT may be conducted on selected bins.

A bin in this regard in particular refers to at least one sample, afrequency or a frequency rage (e.g., a ramp of frequencies) that couldbe associated with a potential target (i.e. at least one potentialtarget). The bin may comprise at least one FFT result (which may beidentified by the CFAR algorithm), it may in particular refer to or bebased on at least one FFT result.

As an option, after the step 206, a non-coherent integration may beconducted. For example, instead of calculating the next FFT result forthe same antenna, the subsequent FFT result for the next antenna may bedetermined, multiplied with a compensation value (if applicable) andadded to the previous sum. Hence, the third stage FFT result may becalculated across the antennas and stored in the buffer. After the lastFFT result for the last antenna is calculated (and multiplied with itscompensation value, if applicable) and added to the buffer, the buffercomprises a non-coherent integration over N antennas. This may then besaved to the memory.

FIG. 1 and FIG. 2 thus show that FFT results (intermediate spectrum) areto be saved to a memory for further processing purposes. Examplespresented herein in particular allow a reduction of the required memory,which may lead to a reduced memory area on a chip and thus to a bettercost-efficiency of the radar device. Another advantage is a reduction ormemory transfer operations, which leads to a reduced computation timeand to a reduced power consumption of the device.

Frequency-modulated continuous-wave radar (FMCW), also referred to ascontinuous-wave frequency-modulated (CWFM) radar, is a short-rangemeasuring radar set capable of determining distances. This increasesreliability by providing distance measurement along with speedmeasurement, which is beneficial in case there is more than one sourceof reflection arriving at the radar antenna. The transmitted signal mayalso be used as a local-oscillator signal for down conversion purposes,wherein an intermediate frequency is proportional to the distance of anobject. Hence, the intermediate frequency spectrum determined by thefirst stage FFT may reveal a range (distance) of potential targets.

Several objects that may appear in the same range after the first stageFFT may be separated by conducting a second stage FFT for each bin ofthe first stage FFT results (and each receiving antenna) therebydetermining the velocity of the respective object.

A third stage FFT based on the velocity bins is used to determine theazimuth angle of the respective object.

Hence, the first stage FFT separates the covered range into bins (alsoreferred to as bin results). Each FFT bin (result) may correspond to areceived signal per antenna based on an emitted radar ramp (an emittedsignal with increasing frequencies), which was processed by the firststage FFT. The second stage FFT may be determined in a direction alongthe ramps based on at least one first stage FFT bin. The third stage FFTmay be determined across the antenna based on at least one second stageFFT bin. Hence, each FFT stage may produce several bins that may besubject for further processing.

Storing bins requires memory. The more bins to be stored, the morememory is required.

Examples provided herein in particular suggest saving memory by notstoring all bins. It may in particular be advantageous to select bins tobe stored and/or bins not to be stored. Not storing a bin may inparticular comprise not storing the data of such bin and/or replacingits value by zero or by a predefined value or threshold.

In case the radar device is part of a vehicle, the speed of the vehiclemay indicate which information is required for determining whether ornot an object is in front of the vehicle. If the vehicle is travellingwithin a city, a distance considerably nearer and a wider angle in frontof the vehicle may be of interest compared to the vehicle driving athigh speed on a highway.

For example, a radar device may determine 1024 samples (bins) for afrequency ramp (i.e. a frequency range from a first to a second value).These 1024 samples may cover a distance amounting to 200 m in front ofthe vehicle. Each bin may thus correspond to a section amounting to ca.20 cm. In this example, measurements in the ranges

-   -   between 0 m and 1 m and    -   between 160 m and 200 m        may be ignored, which would reduce the number of bins (samples)        to be stored by ca. 20%. In terms of memory size, this would        allow using a 1 MB memory instead of a 1.2 MB memory.

FIG. 3 shows an exemplary schematic architecture utilizing a combinationof an FFT engine 301 and a Bin rejection engine 302. A state machine 303is connected to the FFT engine 301 and to the Bin rejection engine 302.FFT results from the FFT engine 301 can be conveyed to an output FIFO305 (FIFO: first-in-first-out, wherein the output FIFO 305 may compriseat least one FIFO register) to a delay unit 304 and to the Bin rejectionengine 302. Results 308 computed by the Bin rejection engine 302 areused to enable writing the FFT results stored in the delay unit 304 tothe output FIFO 305. The Bin rejection engine 302 may be supplied by avalue from a register 307, which allows addressing bins of interestpursuant to the value of the register 307. The output of the FIFO 305 isconveyed to a DMA unit 306 (DMA: direct memory access), which is used towrite results to a memory device.

The Bin rejection engine 302 may comprise an internal address counter,which may be updated each time a new bin of interest is checked.

If the Bin rejection engine 302 is not enabled, the state machine 303 isset such that FFT results from the FFT engine 301 are conveyed via abypass path (preferably at full speed) from the FFT engine 301 to theoutput FIFO 305.

If the Bin rejection engine 302 is enabled, the state machine 303 is setsuch that FFT results are conveyed from the FFT engine 301 to the Binrejection engine 302 for computation, e.g., FFT results may be passed tothe Bin rejection engine 302. For each bin, the Bin rejection engine 302may read the bins of interest from the register 307 to define which binsto be filtered (masked, rejected).

Hence, the Bin rejection engine 302 may suppress or mask certain binsfrom being further processed. These bins can be addressed or selectedvia the register 307. This register 307 may be a memory that allowsselecting bins or groups of bins.

The Bin rejection engine 302 may be arranged in proximity to the FFTengine 301. Both may be arranged on a single device, in particular on asingle chip. The Bin rejection engine 302 may in particular be part ofthe FFT engine 301 or vice versa.

Bins may be removed after the first FFT stage is conducted. This mayresult in a range limitation (see example above) and thus in focusing ona range that may be advantageously used for further processing steps,e.g., a second and/or third FFT stage. By focusing the range (area) tobe processed, the amount of memory required can be significantlyreduced.

The bin rejection may be able to filter out or mask at least one bin.The bin rejection may in particular be able to set at least one bin to apredefined value, e.g., 0, a maximum value, a minimum value. The filter(also referred to as mask) may determine a subset of FFT bins that areto be retained for further processing.

It is noted that the Bin rejection 302 engine may be supplied by suchfilter or mask from the register 307 or from a CPU, a CFAR engine or athreshold module.

The filter could be supplied by software and/or by hardware. The filtermay be based on a CFAR algorithm (e.g., based on objects alreadydetected by CFAR and cutting off ranges where no object was detected).The filter may be subject to a dynamic adaptation based on, e.g., aspeed of a vehicle, surroundings (e.g., city, highway) of the vehicleand/or at least one object detected. The filter may also be determinedbased on a comparison of at least one bin with at least one adjacentbin. This may be achieved by an arithmetic operation.

The filter may comprise (at least) one mask bit for each bin in the FFTresults. Such mask bit may enable various operation modes:

-   (1) Operation Mode: Bin Rejection:    -   If the mask bit is set, the associated bin is retained; if the        mask bit is not set, the associated bin is not stored (i.e.        “rejected”, removed from the results).-   (2) Operation Mode: Zeroing without threshold:    -   If the mask bit is not set, its value is set to zero. In such        case, the associated bin is retained.-   (3) Operation Mode: Zeroing with threshold:    -   If the mask bit is set, the associated bin is retained; if the        mask bit is not set, the value of the associated bin is compared        with a threshold value: If the value of the bin (reaches or) is        above the threshold value, the associated bin is retained,        otherwise the associated bin is rejected. It is noted that        setting its value to zero may be an option for rejecting a bin.        Example: Bin Compression

According to another example, memory may be saved by adapting the FFTengine such that the output of the FFT engine is compressed. In suchcase, the output of the FFT leads to a reduced memory footprint.

The compression may be based on a progressive coding, which results in acombination (compression) of at least one portion of the bins. Thecompression may be conducted based on a characteristic of the human eye,which has a high accuracy for a short range and a lower accuracy for along range. In other words, bins corresponding to a short range may becoded with a higher accuracy compared to bins that correspond to a longrange.

For example, the bins of one frequency ramp cover to a distance of, e.g.200 m ahead of the vehicle, each bin corresponds to a fraction thereof,e.g., to 20 cm. A first group of bins comprise the bins that correspondto the distance up to 70 m, a second group of bins comprise the binsthat correspond to the distance from 70 m to 130 m and a third group ofbins comprise the bins that correspond to the distance from 130 m to 200m. The first group may not be compressed, because of the high accuracythat is favorable for the short range in front of the vehicle. Thesecond group may be compressed with a first compression rate and thethird group may be compressed utilizing a second compression rate. Thesecond compression rate may be higher than the first compression rate.Each compression results in a reduced amount of memory required forstoring bins compared to the scenario when no compression is applied andfull accuracy is used.

Compression may be achieved by various means. One example is to combineFFT bins by adding them. For example, at least two FFT bins may becombined.

This example allows reducing the memory required for storing FFT bins.Also, a single frequency ramp can be used to acquire FFT bins.

A single acquisition cycle may comprise at least on frequency sweep,i.e. a frequency ramp; each ramp is sampled with a predefined samplerate, for instance 1024 samples which defines the size of the firststage FFT. This will result in several raw vector data sets: There willbe as many vector data sets as there are frequency sweeps, typically apower of two as the number of ramps defines the size of the second stageFFT. In case of a 256 ramps acquisition cycle, 256 vectors each with1024 samples will be obtained. After the first stage FFT there willagain be 256 FFTs of 1024 bins each. Then due to the transposeoperation, calculating FFTs along the ramp axle, may result in 1024 FFTsof 256 points.

After the first stage FFT is conducted, range gates are linearlydistributed along the frequency axis of the spectrum; after the secondstage FFT, velocity gates are linearly distributed along the frequencyaxis of the spectrum. The examples described here in particular utilizea non-linear processing of the FFT bins for scenarios (e.g., automotiveradar processing applications) where distance and velocity precision maybe of higher important on close objects than on far objects.

By compressing FFT bins, memory may be saved as only the information inthe FFT spectrum that is required for further processing is stored. FFTbins may be accumulated (e.g., progressively coded by being combinedand/or at least partially omitted) based on a predefined condition. Thismay result in a non-equidistant (i.e. progressive) spectrum.

FIG. 4 shows an exemplary embodiment of a first stage FFT spectrum 401comprising 16 bins “Bin 0” to “Bin 15” and a FFT spectrum 402 that is acompressed version of the FFT spectrum 401. The FFT spectrum 402 may bestored in a random access memory instead of the FFT spectrum 401 therebyleading to reduced amount of memory required. In the example accordingto FIG. 4, the FFT spectrum 402 comprises

-   -   FFT bins Bin 0 to Bin 7 without any compression applied;    -   a compressed bin Bin 8′ based on an addition of the four bins of        the FFT spectrum 401 according to:        -   Bin 8′=Bin 8+Bin 9+Bin 10+Bin 11.    -   a compressed bin Bin 9′ based on an addition of the four bins of        the FFT spectrum 401 according to:        -   Bin 9′=Bin 12+Bin 13+Bin 14+Bin 15.

Hence, storing the FFT spectrum 402 instead of the FFT spectrum 401saves five bins, i.e. requires ca. 31% less memory.

It is noted that the summation for Bin 8′ and Bin 9′ can be applied onthe fly (based on, e.g., the bins being supplied as data stream) withouthaving to provide separate memory space for the respective bins that aresubject to the addition. Hence, no intermediate saving of the bins Bin 9to Bin 15 is required.

In another example, summing 4 bins together for a 256 bin FFT after Bin128 results in 128+128/4=160 bins instead of said 256 bins.

It is also an option to combine a different number of bins forcompression purposes. The number may increase with an increasing rangein front of the vehicle. FIG. 5 shows an example based on FIG. 4,comprising a first stage FFT spectrum 501 comprising 16 bins “Bin 0” to“Bin 15” and a compressed FFT spectrum 502. To compile the FFT spectrum502 an index increment of 1 is used, i.e. the first compression combinestwo bins, the second compression combines three bins, i.e.

-   -   the FFT bins Bin 0 to Bin 7 are not compressed;    -   a compressed bin Bin 8′ is based on an addition of two bins of        the FFT spectrum 501 according to:        -   Bin 8′=Bin 8+Bin 9;    -   a compressed bin Bin 9′ is based on an addition of three bins of        the FFT spectrum 501 according to:        -   Bin 9′=Bin 10+Bin 11+Bin 12; and    -   a compressed bin Bin 10′ is based on an addition of four bins of        the FFT spectrum 501 according to:        -   Bin 10′=Bin 13+Bin 14+Bin 15 (+Bin 16).

In this example, Bin 16 is not available.

FIG. 6 shows an example based on FIG. 4, comprising a first stage FFTspectrum 601 comprising 16 bins “Bin 0” to “Bin 15” and a compressed FFTspectrum 602. To compile the FFT spectrum 602 an index increment of 2 isused, i.e. the first compression combines three bins, the secondcompression combines five bins, i.e.

-   -   the FFT bins Bin 0 to Bin 7 are not compressed;    -   a compressed bin Bin 8′ is based on an addition of two bins of        the FFT spectrum 601 according to:        -   Bin 8′=Bin 8+Bin 9+Bin 10;    -   a compressed bin Bin 9′ is based on an addition of three bins of        the FFT spectrum 601 according to:        -   Bin 9′=Bin 11+Bin 12+Bin 13+Bin 14+Bin 15.

In an exemplary scenario, a distance of 200 m ahead of a vehicle may beprocessed by a number of 1024 FFT bins, each FFT bin corresponding to

-   -   200 m/1024≈20 cm.

In such use case, the first 100 m may be processed with full precision(without compression of the FFT bins) and the next 100 m (from 100 m to200 m range) may be processed with ¼ precision, i.e. a compressionfactor of four. In total, this would lead to a memory reduction for thefull range from 0 m to 200 m of 50%.

In another example, the processing of the FFT bins may be as follows:

-   -   from 0 m to 70 m: full precision, no compression;    -   from 70 m to 100 m: ½ precision (compression factor 2);    -   from 100 m to 200 m: ¼ precision (compression factor 4),        which would require only ca. 59% of the memory compared to the        scenario without bin compression.

Hence, the compression of the FFT bins may start after a predefined binindex. In the examples shown in FIG. 4 to FIG. 6 the compression startsafter Bin 7. The number of bins to be processed in each compressionportion (i.e. Bin 8′, Bin 9′ or Bin 10′ according to FIG. 4 to FIG. 6)may be constant (as shown in FIG. 4) or it may vary (as shown in FIG. 5or in FIG. 6). It is noted that the linear increments shown in FIG. 5and FIG. 6 are only examples of progressive compression schemes. It isalso possible to apply different non-linear increments/decrements orvarying patterns.

It is noted that the operation on the FFT bins may be a summation oraccumulation of complex numbers in case the FFT bins are complexnumbers. It is an option that a signal power of the FFT bins may beprocessed, e.g., accumulated.

The examples suggested herein may in particular be based on at least oneof the following solutions. In particular combinations of the followingfeatures could be utilized in order to reach a desired result. Thefeatures of the method could be combined with any feature(s) of thedevice, apparatus or system or vice versa.

A method for processing radar signals is suggested, wherein said radarsignals comprise digitized data received by at least one radar antenna,the method comprising:

-   -   determining FFT results based on the digitized data received;    -   storing a first group of the FFT results, wherein the first        group of FFT results comprises at least two portions, wherein a        first portion of FFT results is stored with a first accuracy and        a second portion of FFT results is stored with a second        accuracy.

The FFT results may also be referred to as FFT bins.

The first accuracy and the second accuracy may each be or be related toa compression level applied to the FFT results. The different portionsof the first group that are stored in the memory have different levelsof compression. This allows providing a progressive compression schemedepending on a predefined condition, e.g., a speed information, anenvironment information or a result of a CFAR information.

It is noted that there may be more than two portions of differentcompression. Advantageously, one portion of the first group comprisesfirst stage FFT results that are not subject to any compression. TheseFFT results thus show the highest accuracy possible based on the sampleddata.

According to one example, at least one portion of FFT results of thefirst group may comprise compressed data.

In an embodiment, the first accuracy corresponds to FFT results withoutcompression.

In an embodiment, the second accuracy is lower than the first accuracy.

In an embodiment, the FFT results of the second portion are determinedby combining FFT results based on the digitized data.

Combining the FFT results may be adding several first stage FFT results.Such combining may comprise a weighting mechanism, by multiplying eachof the FFT results to be added with a predetermined value.

In an embodiment, the second accuracy is lower than the first accuracyand the first accuracy corresponds to a first range and the secondaccuracy corresponds to a second range, wherein the first range iscloser than the second range.

The first range may be closer to a vehicle with a radar device than thesecond range.

In an embodiment, the first group of FFT results comprises more than twoportions with different accuracies, wherein the different accuraciesincrease with an increasing distance.

Hence, the farther away from the radar device, the less accuracy (highercompression rate) may be used for the respective portion of FFT results.This results in one example of a progressive compression scheme, whichneeds less memory for storing more distant FFT results.

In an embodiment, the method further comprises:

-   -   determining the accuracy for each portion of FFT results based        on a predefined condition.

In an embodiment, the predefined condition comprises a speedinformation.

In an embodiment, the predefined condition comprises an environmentinformation.

In an embodiment, the predefined condition comprises a CFAR information.

In an embodiment, the method further comprises:

-   -   storing the first group of the FFT results without a second        group of the FFT results.

The second group of FFT results may be filtered out (masked, rejected).These second group FFT results may in particular not be stored and maynot be subject for further processing.

Storing the first group of FFT results without at least one portion mayin particular be implemented such that only the FFT results that aredifferent from such at least one portion are stored. Hence, the secondgroup of FFT results may not be stored, which results in a reducedrequirement for memory space.

Each of the FFT results (FFT bins) may be based on a first stage FFT andcorrespond to a particular section with regard to a distance covered bythe received radar signal. Hence, an emitted (frequency modulated) radarsignal (as used by, e.g., a FMCW radar) may be received and digitallysampled. Each sample in the frequency domain may correspond to a portionof the distance, e.g., ahead of a vehicle from which the radar signal isemitted.

As an option, more than one receiving radar antenna may be provided anddigitized data may be obtained for each such antenna.

In an embodiment, the method further comprises:

-   -   processing the first group of FFT results.

Hence, only the first group of FFT results may be stored and furtherprocessed, e.g., by a second stage FFT and/or a third stage FFT.

In an embodiment, the FFT results are first stage FFT results.

In an embodiment, the method further comprises:

-   -   determining the first group of FFT results and/or the second        group of FFT results based on a predefined condition.

It is noted that either the first group of FFT results (to be stored inthe memory) or the second group of FFT results (not to be stored in thememory) may be determined. It may be sufficient to determine one of thegroups. As an option, both groups may be determined

In an embodiment, the predefined condition comprises a speedinformation.

For example, a speed of a vehicle operating the radar device may beconsidered by the predefined condition: Depending on the speed of thevehicle, the first group of FFT results (and/or the second group of FFTresults) may be determined For example, if the vehicle travels athighway speed, a distance ahead of the vehicle from 70 m to 200 m may beconsidered, wherein the distance 0 m to 70 m may not be processed.Hence, the second group of FFT results corresponding to the neardistance from 0 m to 70 m are not stored in the memory. If the vehicleslows down, the distance 0 m to 100 m may be considered, whereas thedistance from 100 m to 200 m is not subject to further processing.

In an embodiment, the predefined condition comprises an environmentinformation.

The environment information may indicated the surroundings of thevehicle, e.g., whether the vehicle is in a city or on a highway. Theenvironment information may be determined by a camera and/or anavigation system.

In an embodiment, determining the FFT results and determining and thefirst group of FFT results and/or the second group of FFT results isprovided by a single device, in particular a single chip.

The hardware determining the FFT results of the second group may bearranged in the vicinity of the hardware providing the FFT. Both may bearranged on the same device, die or chip.

In an embodiment, the predefined condition comprises an CFARinformation.

Hence, the filter may be dynamically adjusted depending on the outcomeof a CFAR operation: If an object is detected, this object or an area(distance) around this object is subject for further processing, whereasan area (distance) where no object was detected may be subject to binrejection.

In an embodiment, the method further comprises:

-   -   determining the second group of FFT results based on a mask bit,        wherein one mask bit is provided for each FFT result of the        first group and of the second group.

This allows enabling various operation modes: For example, if the maskbit for a FFT result is set, this may indicate that the FFT resultbelongs to the first group; if the mask bit is not set, it may belong tothe second group. Of course, this approach may be applied vice versa,i.e. if the mask bit for a FFT result is set, it may indicate that thisFFT result belongs to second group and if the mask bit is not set, theFFT result may belong to the first group.

It is also possible to compare the FFT result with a predefined value inorder to determine whether it belongs to the first group or the secondgroup of FFT results.

Also, a device for processing radar signals is provided, said devicecomprising

-   -   a FFT engine for determining FFT results based on digitized data        received from at least one antenna;    -   a compression engine for storing a first group of the FFT        results, wherein the first group of FFT results comprises at        least two portions, wherein a first portion of FFT results is        stored with a first accuracy and a second portion of FFT results        is stored with a second accuracy.

In an embodiment, the FFT engine and the compression engine are arrangedon a single component, in particular on a single chip.

In an embodiment, the device further comprises

-   -   a bin rejection engine for storing the first group of the FFT        results without a second group of the FFT results.

It is noted that the compression engine may be part of the bin rejectionengine or vice versa. It is in particular an option that one physicalentity or several physical entities are provided for the services of thecompression engine and the bin rejection engine.

In an embodiment, the FFT engine and the bin rejection engine arearranged on a single component, in particular on a single chip.

Further, a device for processing radar signals is suggested, whereinsaid radar signals comprise digitized data received by at least oneradar antenna, such device comprising:

-   -   means for determining FFT results based on the digitized data        received;    -   means for storing a first group of the FFT results, wherein the        first group of FFT results comprises at least two portions,        wherein a first portion of FFT results is stored with a first        accuracy and a second portion of FFT results is stored with a        second accuracy.

In an embodiment, the device further comprises:

-   -   means for storing the first group of the FFT results without at        second group of the FFT results.

A computer program product is provided, which is directly loadable intoa memory of a digital processing device, comprising software codeportions for performing the steps of the method as described herein.

In one or more examples, the functions described herein may beimplemented at least partially in hardware, such as specific hardwarecomponents or a processor. More generally, the techniques may beimplemented in hardware, processors, software, firmware, or anycombination thereof. If implemented in software, the functions may bestored on or transmitted over as one or more instructions or code on acomputer-readable medium and executed by a hardware-based processingunit. Computer-readable media may include computer-readable storagemedia, which corresponds to a tangible medium such as data storagemedia, or communication media including any medium that facilitatestransfer of a computer program from one place to another, e.g.,according to a communication protocol. In this manner, computer-readablemedia generally may correspond to (1) tangible computer-readable storagemedia which is non-transitory or (2) a communication medium such as asignal or carrier wave. Data storage media may be any available mediathat can be accessed by one or more computers or one or more processorsto retrieve instructions, code and/or data structures for implementationof the techniques described in this disclosure. A computer programproduct may include a computer-readable medium.

By way of example, and not limitation, such computer-readable storagemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage, or other magnetic storage devices, flashmemory, or any other medium that can be used to store desired programcode in the form of instructions or data structures and that can beaccessed by a computer. Also, any connection is properly termed acomputer-readable medium, i.e., a computer-readable transmission medium.For example, if instructions are transmitted from a website, server, orother remote source using a coaxial cable, fiber optic cable, twistedpair, digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio, and microwave are included in the definition of medium. It shouldbe understood, however, that computer-readable storage media and datastorage media do not include connections, carrier waves, signals, orother transient media, but are instead directed to non-transient,tangible storage media. Disk and disc, as used herein, includes compactdisc (CD), laser disc, optical disc, digital versatile disc (DVD),floppy disk and Blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media.

Instructions may be executed by one or more processors, such as one ormore central processing units (CPU), digital signal processors (DSPs),general purpose microprocessors, application specific integratedcircuits (ASICs), field programmable logic arrays (FPGAs), or otherequivalent integrated or discrete logic circuitry. Accordingly, the term“processor,” as used herein may refer to any of the foregoing structureor any other structure suitable for implementation of the techniquesdescribed herein. In addition, in some aspects, the functionalitydescribed herein may be provided within dedicated hardware and/orsoftware modules configured for encoding and decoding, or incorporatedin a combined codec. Also, the techniques could be fully implemented inone or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs (e.g., a chip set). Various components,modules, or units are described in this disclosure to emphasizefunctional aspects of devices configured to perform the disclosedtechniques, but do not necessarily require realization by differenthardware units. Rather, as described above, various units may becombined in a single hardware unit or provided by a collection ofinteroperative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Although various exemplary embodiments of the invention have beendisclosed, it will be apparent to those skilled in the art that variouschanges and modifications can be made which will achieve some of theadvantages of the invention without departing from the spirit and scopeof the invention. It will be obvious to those reasonably skilled in theart that other components performing the same functions may be suitablysubstituted. It should be mentioned that features explained withreference to a specific figure may be combined with features of otherfigures, even in those cases in which this has not explicitly beenmentioned. Further, the methods of the invention may be achieved ineither all software implementations, using the appropriate processorinstructions, or in hybrid implementations that utilize a combination ofhardware logic and software logic to achieve the same results. Suchmodifications to the inventive concept are intended to be covered by theappended claims.

The invention claimed is:
 1. A method for processing radar signals,wherein said radar signals comprise digitized data received by at leastone radar antenna, the method comprising: determining Fast FourierTransform (FFT) results based on the digitized data received; andstoring a first group of the FFT results in a memory, wherein the firstgroup of FFT results comprises at least two portions, wherein a firstportion of the FFT results correspond to a first range of distances andis stored in accordance with a first compression rate yielding a firstcoded accuracy, wherein a second portion of the FFT results correspondto a second range of distances and is stored in accordance with a secondcompression rate that is higher than the first compression rate, thesecond compression rate yielding a second coded accuracy, and whereinthe second portion of the FFT results are stored in the memory inaccordance with the second compression rate based upon a summation ofother FFT results within the second range of distances to utilize lessmemory compared to separately storing the other FFT results.
 2. Themethod according to claim 1, wherein the first coded accuracycorresponds to the FFT results in accordance with the first compressionrate yielding no compression.
 3. The method according to claim 1,wherein the second coded accuracy is lower than the first codedaccuracy.
 4. The method according to claim 1, wherein the FFT results ofthe second portion are determined by combining the FFT results based onthe digitized data.
 5. The method according to claim 1, wherein thesecond coded accuracy is lower than the first coded accuracy, andwherein the first range of distances is closer than the second range ofdistances.
 6. The method according to claim 1, wherein the first groupof FFT results comprises more than two portions with different codedaccuracies, and wherein the different coded accuracies increase with anincreasing distance.
 7. The method according to claim 1, furthercomprising: determining the coded accuracy for each portion of the FFTresults based on a predefined condition.
 8. The method according toclaim 7, wherein the predefined condition comprises speed information.9. The method according to claim 7, wherein the predefined conditioncomprises environment information.
 10. The method according to claim 7,wherein the predefined condition comprises Constant False AlarmRejection (CFAR) information.
 11. The method according to claim 1,further comprising: storing the first group of the FFT results without asecond group of the FFT results.
 12. The method according to claim 11,further comprising: processing the first group of FFT results.
 13. Themethod according to claim 11, wherein the FFT results are first stageFFT results.
 14. The method according to claim 11, further comprising:determining the first group of FFT results and/or the second group ofFFT results based on a predefined condition.
 15. The method according toclaim 14, wherein the predefined condition comprises speed information.16. The method according to claim 14, wherein the predefined conditioncomprises environment information.
 17. The method according to claim 14,wherein (i) determining the FFT results, (ii) determining the firstgroup of FFT results, and/or (iii) determining the second group of FFTresults is performed by a single chip.
 18. The method according to claim14, wherein the predefined condition comprises Constant False AlarmRejection (CFAR) information.
 19. The method according to claim 14,further comprising: determining the second group of FFT results based ona mask bit, wherein one mask bit is provided for each FFT result of thefirst group and of the second group.
 20. A device for processing radarsignals, comprising: one or more processors configured to determine FastFourier Transform (FFT) results based on digitized data received from atleast one antenna; and a memory configured to store a first group of theFFT results, wherein the first group of FFT results comprises at leasttwo portions, wherein a first portion of FFT results correspond to afirst range of distances and is stored in accordance with a firstcompression rate yielding a first coded accuracy and a second portion ofFFT results correspond to a second range of distances and is stored inaccordance with a second compression rate yielding a second codedaccuracy, wherein the second portion of the FFT results are stored inthe memory in accordance with the second compression rate based upon asummation of other FFT results within the second range of distances toutilize less memory compared to separately storing the other FFTresults.
 21. The device according to claim 20, wherein the one or moreprocessors are arranged on a single chip.
 22. The device according toclaim 20, wherein the one or more processors are further configured tostore the first group of the FFT results in the memory without a secondgroup of the FFT results.
 23. A device for processing radar signals,wherein said radar signals comprise digitized data received by at leastone radar antenna, comprising: means for determining Fast FourierTransform (FFT) results based on the digitized data received; and meansfor storing a first group of the FFT results, wherein the first group ofFFT results comprises at least two portions, and wherein a first portionof FFT results correspond to a first range of distances and is stored inaccordance with a first compression rate yielding a first coded accuracyand a second portion of FFT results corresponding to a second range ofdistances and is stored in accordance with a second compression ratethat is higher than the first compression rate, the second compressionrate yielding a second coded accuracy, wherein the second portion of theFFT results are stored in accordance with the second compression ratebased upon a summation of other FFT results within the second range ofdistances to utilize less memory compared to separately storing theother FFT results.
 24. The device according to claim 23, furthercomprising: means for storing the first group of the FFT results withouta second group of the FFT results.
 25. The method according to claim 1,wherein the second portion of the FFT results includes each of the FFTresults within the second range of distances.
 26. The method accordingto claim 1, wherein the FFT results are first stage FFT resultsperformed as part of a first stage FFT, and further comprising:performing a second stage FFT using the first stage FFT results toprovide second stage FFT results indicative of velocity information; andperforming a third stage FFT using the second stage FFT results toprovide third stage FFT results indicative of azimuth angle information.